56 results about "Discrete optimization" patented technology

The invention relates to an unmanned aerial vehicle control system and method for formation flight in a wind environment. The method comprises the following steps: generating a wind disturbance environment with a specified map scale and containing random obstacles; acquiring external image data and inertial measurement data of the unmanned aerial vehicle, and generating a grid map through point cloud extraction and conversion operation; the method comprises the following steps: modeling a spatial topological structure of an unmanned aerial vehicle cluster by adopting an undirected graph mode, and solving and generating a collision-free initial trajectory by adopting an unconstrained discrete optimization mode; optimizing the initial trajectory by adopting a task redistribution strategy, and re-planning a global target and a local target to obtain a re-planned optimized trajectory; and based on the optimized trajectory, solving by adopting a model prediction control mode to obtain an optimal control quantity for unmanned aerial vehicle bottom layer control so as to correspondingly control the motion state of each unmanned aerial vehicle and realize formation flight. Compared with the prior art, the unmanned aerial vehicle cluster can efficiently and accurately realize formation flight in an obstacle environment with wind influence.

The invention belongs to the technical field of additive manufacturing, and particularly relates to a multi-scale parallel optimization method for a lightweight structure based on lattice discrete optimization. According to the method, through multi-scale parallel optimization design, a macro structure and a micro lattice structure are described through geometric parameters of a movable deformation rod piece, an improved two-value coding parameterization method, called a BCP method for short, is adopted to solve discrete optimization of the micro lattice structure, and geometric parameters of an optimization result are easy to extract.

The invention provides a plane layout optimization method and device based on CAD drawing automatic identification, relates to the field of plane layout optimization, and solves the technical problems that only non-overlapping constraints among equipment are considered, actual space obstacles cannot be automatically avoided, and a generated layout scheme cannot be implemented. The method comprises the following steps: extracting barrier data in a layer; and through a parameter configuration mechanism, generating an equipment parameter information structural body containing the mapping relationship between the equipment and the process. And establishing a continuous optimization model containing an IPOPT solver, and obtaining an initial layout solution containing the relative orientation of each device. And based on the relative orientation of each device in the initial layout solution, introducing an obstacle rejection constraint and a device non-overlapping constraint to construct a discrete optimization model, obtaining the center position coordinates of each device through a Gurobi solver, and integrating a dynamic adjustment mechanism and an interlayer relationship optimization mechanism. And generating a new layout map for visual output. The method and the device are used in a plane layout optimization process.

A method for managing channels including, by a computing device, receiving a join request from a user corresponding to a request to establish one or more channels with respective user nodes of a plurality of user nodes in a second-layer payment protocol network configured to process transactions between users associated with a cryptocurrency network, obtaining information about the second-layer payment protocol network that indicates channels established between the plurality of user nodes and at least one of balances associated with the plurality of user nodes in the second-layer payment protocol network or balances associated with channels between two or more of the plurality of user nodes, simulating, based on the information obtained about the second-layer payment protocol network, a plurality of transactions between selected user nodes of the plurality of user nodes to obtain transaction simulation results that include at least reliability scores associated with the plurality of user nodes, identifying one or more recommended user nodes based on the transaction simulation results and the information obtained about the second-layer payment protocol network, and providing, to the user, an output indicating the one or more recommended user nodes.

The invention relates to the technical field of aircraft path planning, and particularly provides an aircraft path planning method for a space non-cooperative target. The method comprises the following steps: constructing an aircraft dynamics model and constraint conditions, and obtaining a non-convex optimal control problem; performing discretization processing on a non-convex optimal control problem through a non-equal-interval pseudo-spectrum discrete optimization model, and converting the non-convex optimal control problem into a fixed-time nonlinear programming problem about discrete nodes; according to the method, a fixed time nonlinear programming problem is convexed to form a convex sub problem, a local optimal solution of a non-convex optimal control problem is obtained through sequence iteration, under the multi-state constraint, path planning of an aircraft approaching a space non-cooperative target is achieved, the problem of no feasible solution caused by non-accurate linearization is solved, and the method is suitable for being applied to a non-cooperative target. And the convergence rate of the algorithm is accelerated.

A system for identifying approximate solutions to discrete optimization problems includes a quantum computing device. The quantum computing device iteratively configures a layered quantum circuit to evaluate a Hamiltonian representation of a cost function over a set of parameter values. After each iteration, the quantum computing device identifies a new parameter value based upon an estimate of an expectation value under the quantum state output by execution of the layered quantum circuit. The quantum computing device updates a configuration of the layered quantum circuit based upon the new parameter value. After a final iteration, the output quantum state of the layered quantum circuit is measured to identify an approximate solution vector. This solution vector is output as optimization results that are indicative of an approximate solution to the discrete optimization problem.

The invention discloses a post-disaster city full-coverage detection method based on a heterogeneous unmanned system, and belongs to the technical field of rescue, and the method comprises the steps: taking an unmanned plane and an unmanned vehicle as the heterogeneous unmanned system, and constructing a full-coverage path planning model of the heterogeneous unmanned system; discretizing the full-coverage path planning model of the heterogeneous unmanned system to obtain a discrete optimization model of the full-coverage path planning of the heterogeneous unmanned system; an improved memetic algorithm is adopted to optimize the on-off state of path points in the discrete optimization model, an optimal open point set is obtained, a mower algorithm is adopted to traverse the optimal open point set, and a shortest full-coverage detection path is obtained; and constructing a charging point dynamic adjustment mechanism, and performing full-coverage detection based on the shortest full-coverage detection path and the charging point dynamic adjustment mechanism. According to the method, the global search capability and the convergence speed of the memetic algorithm are effectively improved, a two-way convergence charging point dynamic adjustment strategy is designed, and the charging waiting time is effectively shortened.

The present application belongs to the technical field of additive manufacturing, and particularly relates to a lightweight structure multi-scale parallel optimization method based on lattice discrete optimization. The present application uses the geometric parameters of movable and deformable rods to describe macroscopic structures and microscopic lattice structures through multi-scale parallel optimization design, and uses an improved double-value coding parameterization method, referred to as BCP method, to solve the discrete optimization of the microscopic lattice structure and easily extract the geometric parameters of the optimization results.

The invention discloses a renewable energy source data clustering method and system based on an optical quantum computer, and the method comprises the steps: building a renewable energy source data clustering optimization problem based on a quadratic unconstrained binary optimization (QUBO) model based on a discrete optimization objective function, carrying out the matrix of the objective function, carrying out the clustering optimization of the objective function through a kernel method, and carrying out the clustering optimization of the objective function. The method comprises the following steps of: establishing a target function item, converting the target function item into a matrix form, constructing a matrix of a new clustering model, finally solving a new QUBO optimization problem by using CIM, and converting the new QUBO optimization problem into an optimal clustering result. According to the method, data similarity measurement is improved by constructing the QUBO model and combining a kernel method, and a renewable energy source data clustering problem is converted into a form suitable for CIM solution, so that the calculation bottleneck of a classical calculation method in high-dimensional data processing is avoided. According to the method, the nonlinear characteristic of the data can be kept, efficient clustering optimization calculation can be realized, and the calculation efficiency and the clustering precision are remarkably improved.

The invention discloses a tumor ablation decision-making method and system driven by CT big data. The method comprises the following steps: acquiring CT image data of a patient, preprocessing, and extracting to obtain an anatomical structure feature identifier set; calculating possible puncture path connecting lines to obtain a candidate path space set; adopting a discrete optimization algorithm to obtain an optimal solution of the puncture path; managing a needle point and a needle entry point of the ablation needle, and controlling an intraoperative needle passage to accord with a planned path; calculating through a heat conduction equation, generating and updating an ablation thermal field model, and dynamically displaying ablation thermal field distribution; and calculating an ablation index, and adjusting an ablation parameter or a puncture path. The system comprises a data set extraction module, a puncture path connecting line calculation module, an optimal space set module, a puncture path optimal solution calculation module, a needle passage control module, a thermal field distribution display module and an adjustment module. The precision and safety of tumor ablation can be greatly improved, excessive ablation or incomplete ablation is avoided, and damage to normal tissue is reduced.

This invention provides a multi-objective relay selection method for energy harvesting cognitive relay networks. In the context of energy harvesting cognitive relay networks, a new multi-objective function is constructed to solve discrete optimization problems by comprehensively considering network energy efficiency and signal-to-noise ratio. The multi-objective relay selection method is then rapidly obtained through a multi-objective quantum monarch butterfly optimization mechanism. This method maximizes network energy efficiency while ensuring the signal-to-noise ratio, providing a new idea and method for solving relay selection schemes in energy harvesting cognitive relay networks.

This invention relates to the field of valve detection technology, and more particularly to a method for detecting internal leakage in valves based on acoustic emission signals. The method includes: acquiring the topology and geometric parameters of the pipeline network to determine the pipeline location characteristics of each valve; based on these characteristics, analyzing the propagation characteristics of acoustic emission signals in the pipe segment where the valve is located for at least two preset frequency bands, and defining the effective monitoring arc segment for each valve in different frequency bands; establishing a dual-modal coverage matrix between the valve and candidate sensor locations, combined with the background noise level; constructing a discrete optimization function based on this matrix to obtain the sensor installation location combination; deploying sensors to collect signals, extracting multi-frequency band time-frequency domain features, and constructing a dual-modal feature vector; combining the feature vector and the coverage matrix to calculate the probability that each valve is a leakage source, and identifying the valve with internal leakage. This invention achieves accurate detection and location of internal leakage in valves by optimizing sensor layout using a dual-modal coverage matrix and fusing multi-frequency band features.

The invention discloses a discrete combination optimization control method of a switchable series-parallel current limiting circuit based on an IGBT and a contactor, and relates to the technical field of power electronic control and electrical testing, the control method comprises the following steps: S1, a real-time sensing step: collecting a target current limiting value and a bus voltage of a system, and real-time state data of each reconfigurable power module, wherein the real-time state data at least comprises a current value, a voltage value and a device temperature. According to the discrete combination optimization control method of the switchable series-parallel current limiting circuit based on the IGBT and the contactor, by dynamically sensing a real-time working condition and constructing a discrete optimization model containing multiple targets, a scheme meeting static and dynamic performance requirements can be automatically and accurately screened out from massive possible topologies; the problems that a traditional fixed topology circuit is limited in adjusting range and insufficient in precision are solved, and the response reliability and the control accuracy of the system under the wide-range working condition are ensured.

The invention belongs to the technical field of data processing, and provides a power market price prediction method and system based on two-stage optimization, and the method comprises the steps: determining an initial generating capacity node value and an initial unit price node value of each generator set, and carrying out the interpolation refining operation of the initial generating capacity node value and the initial unit price node value, obtaining a refined generating capacity node value and a refined unit price node value; establishing an output interval prediction 0-1 planning model of the generator set; performing discrete optimization solution on the output interval prediction 0-1 planning model to obtain a predicted generating capacity interval of each generator set; according to the predicted generating capacity interval, the refined generating capacity node value and the refined unit price node value, establishing an average electricity price prediction quadratic programming model; and performing continuous optimization solution on the average electricity price prediction quadratic programming model to obtain an average electricity price prediction value. The scheme can adapt to market dynamic changes, and prediction precision, flexibility and reliability are effectively improved.

The present invention discloses a structural interval reliability analysis method based on a Chebyshev expansion truncation model. The method first establishes a Chebyshev expansion truncation approximate model based on the general law of Chebyshev expansion coefficients, determines the Chebyshev test design points, solves the partial coefficients of the Chebyshev approximate model, and uses the approximate model to replace the original high-dimensional and complex functional function. Then, combined with a multi-factor full-level test design method and a discrete optimization algorithm, the upper and lower limits of the Chebyshev expansion truncation approximate model and the reliability of the structure are calculated. The present invention has good versatility and adaptability when using a surrogate function method to evaluate the reliability and safety of complex engineering structures in fields such as civil engineering, mechanical engineering, and aerospace based on interval analysis, greatly reducing the amount of calculation required for structural analysis and simulation during reliability analysis and improving computational efficiency.

The present invention proposes an adaptive inverse design method for photonic devices based on topology optimization. During the design process, an adaptive projection function with both differential processing characteristics and dynamic adjustment characteristics is adopted. On the basis of improving the design efficiency of photonic devices, the present invention ensures that the photonic devices obtained by inverse design have excellent performance indicators. In addition, the designed photonic devices are fully binary, eliminating additional discrete optimization. This not only solves the problem of the degradation of the quality factor of photonic devices caused by discrete optimization, but also simplifies the design process. Further, the photonic devices obtained by inverse design of the present invention have small physical sizes, are easy to scale and integrate, and can be fabricated using conventional semiconductor lithography processes, which is expected to promote the development of photonic integrated chips, especially silicon-based photonic integrated chips.

The present invention discloses an anti-interference decision-making method based on the quantum capuchin monkey mechanism, which relates to the technical field of anti-interference decision-making. The technical key points of the present invention include: constructing an anti-interference decision-making model, which is a combination of the channel, coding method, modulation method and transmission power of the communication party; setting an objective function and constraint conditions based on the anti-interference decision-making model; using the quantum capuchin monkey mechanism to optimize and solve the objective function to obtain the optimal combination of the channel, coding method, modulation method and transmission power. The quantum capuchin monkey mechanism effectively improves the problems of the original capuchin monkey search mechanism, such as the slow convergence speed and the easy entrapment in local optimal solutions due to the too large search space when solving high-dimensional discrete optimization problems, and breaks through the problem that the original capuchin monkey search mechanism is difficult to handle the coexistence of discrete optimization and continuous optimization. The quantum capuchin monkey mechanism proposed by the present invention has obvious advantages in terms of convergence speed, convergence accuracy and finding the global optimal solution.

The invention discloses a man-machine collaborative high-real-time function test cross-domain optimization method and device and a medium, and relates to the technical field of electronic information, and the method comprises the steps: expanding a discrete optimization model into a continuous optimization model, the probability that multiple intermediate events in the fault tree fail at the same time is set to be the minimum value of the test proportion corresponding to all the intermediate events contained in the intersection item, and a continuous optimization model based on intersection item consideration is obtained; the method comprises the following steps: performing theoretical analysis on an inherent reliability calculation formula of a system, and cutting off the inherent reliability calculation formula of the system in combination with expert experience to obtain a continuous optimization model based on real-time consideration; a continuous optimization model based on additional constraints is obtained by additionally establishing a relation between decision variables and inherent reliability of the system; solving the continuous optimization model based on the additional constraint to obtain an optimal test strategy; according to the function test cross-domain optimization method and device and the medium, the real-time problem of model solving is solved, and the cross-domain overall optimization capability of the model is improved.

Techniques are described for processing measurement information to select a respective label for multiple voxels or pixels of a medical image dataset. Reference information is described comprising a respective reference dataset for each one of the possible labels, and each label is associated with a material parameter. A respective label for the respective voxel or pixel is selected by using a discrete optimization algorithm to optimize a cost function comprising a first partial cost function and a second partial cost function, and the incorporation of the second partial cost function into the cost function introduces a coupling between the labels at different voxels or pixels.

Techniques for configuring an entity are disclosed. The techniques include generating, based on multiple sets of features inputted into a machine learning model, predictions of multiple sets of performance metrics for multiple configurations of the entity. The techniques also include calculating a change in efficiency between each configuration in the multiple configurations and the base configuration based on a first set of performance metrics for the base configuration and a second set of performance metrics generated by the machine learning model for the configuration. The techniques further include determining, based on values of the change in efficiency calculated for the multiple configurations, a second set of configuration values with a highest increase in efficiency over the base configuration. The techniques additionally include causing the second set of configuration values to be outputted in association with the entity.

The invention discloses a fine-grained visual classification method and system based on adaptive attention quantization Transform. The system comprises an adaptive quantization selection module, a background elimination module and a dynamic hybrid optimization module. The self-adaptive quantization selection module dynamically selects attention scores of a plurality of attention heads by introducing a global and learnable threshold value and adopting a binary quantization mode, selects the most distinguishing feature, and effectively reduces the dilution of attention of the attention head focused on a non-distinguishing mark on a distinguishing area. And meanwhile, discrete optimization is carried out by adopting a straight-through estimator, so that gradient back propagation is realized. And the background elimination module removes features containing background information, so that interference of background noise is eliminated. And the dynamic hybrid optimization module dynamically optimizes and integrates selection and elimination branches so as to realize an optimal classification effect. Experiments prove that the method achieves the current advanced performance on the fine-grained visual classification task.

The invention discloses a traffic signal control optimization method, system, equipment and medium, and relates to the technical field of traffic management, and the method comprises the steps: obtaining vehicle trajectory data, and carrying out the flow reduction processing of the trajectory data, and obtaining the traffic flow; on the basis of traffic flow, signal timing and vehicle speed parameters are introduced, and delay of signals at different intersections is generated in combination with a support vector regression algorithm; the total delay time is minimized by adjusting the phase difference of signals at different intersections, the particle swarm optimization algorithm is improved by introducing dynamic target weight adjustment, the weight ratio between delay and traffic efficiency is adjusted according to the evolutionary process of particles and the current search condition, and the optimal traffic signal control scheme is obtained. According to the algorithm, through a dynamic weight adjustment mechanism and a particle forgetting mechanism, the convergence speed and the global search capability of a phase difference optimization process can be improved, and the defects of a traditional PSO algorithm in a discrete optimization problem are overcome.

The invention discloses a contact-bending performance collaborative optimization design method of a face gear pair under consideration of spatial constraint, which comprises the following steps: step 1, defining a design variable feasible region: determining a design variable optimization space of the face gear pair based on spatial constraint conditions, design variables including continuous optimization variables and discrete optimization variables; 2, a multi-objective optimization objective function is constructed, wherein the minimum tooth surface maximum contact stress and the minimum tooth root maximum bending stress serve as optimization objectives; step 3, configuring a multi-objective optimization algorithm, realizing global performance optimization of the face gear pair through collaborative optimization of spatial constraint and bearing performance, and solving to obtain optimal design parameters; and 4, designing the face gear pair based on the optimal design parameters. According to the contact-bending performance collaborative optimization design method of the face gear pair under the consideration of the spatial constraint, the global performance optimization of the face gear pair design is realized by establishing a collaborative optimization mechanism of the spatial geometric constraint and the multi-target bearing performance.

The present invention discloses an electric CPS intrusion detection system and method based on multi-objective variable-length CNN discrete optimization. The pre-processed historical monitoring data of the electric information-physical system is used as the input data set, and the number of convolution modules, model topology, architectural parameters of each convolution module and batch size, learning rate, optimizer type, weight regularization and other hyperparameters used to construct the convolutional neural network model are variable-length discretely encoded. The indicators of the CNN model on the validation set and the number of model floating-point operations are used as optimization targets. A multi-objective discrete optimization method is designed to perform multi-objective parallel optimization on the CNN model architecture and parameters based on the variable-length convolution module, thereby obtaining a Pareto optimal CNN model that takes into account both model performance and model complexity. For the real-time monitoring data of the electric CPS, the optimal CNN model is deployed online, thereby realizing online intrusion detection of the electric CPS. The present invention reduces the complexity of the model while ensuring high-precision performance indicators of the model.

The invention provides a photovoltaic photo-thermal panel heat dissipation efficiency improving method based on a neural network and cooling liquid discrete optimization. The method is mainly applied to the technical field of new energy technologies and artificial intelligence. According to the method, a calculation model between the dispersion degree of a cooling liquid inlet and a cooling liquid outlet and the heat exchange efficiency of the photovoltaic photo-thermal plate is established, and the influence of the cooling liquid flow characteristics on the heat exchange performance is analyzed and optimized in combination with the fitting capacity of a neural network. Firstly, a neural network model with cooling liquid inlet and outlet temperature difference, flow distribution and discrete region characteristics as input variables is constructed, the model is trained by using experimental data, and the influence of different discrete parameter combinations on the heat dissipation efficiency is accurately predicted. Through multi-physics field coupling analysis, a neural network model is combined with a finite element simulation result, and the model precision is further verified and improved. Afterwards, discrete parameter indexes obtained through prediction are introduced into a multi-objective optimization framework, a TOPSIS method and grey correlation analysis (GRA) are used for comprehensively evaluating the contribution degree of each parameter to the heat dissipation efficiency, and the optimal cooling liquid flow characteristic and discrete distribution area are determined. And on the basis, generating a group of parameter configuration schemes conforming to the target heat dissipation performance. Through the optimized parameter scheme, the heat dissipation performance of the photovoltaic photo-thermal plate is remarkably improved, and meanwhile the operation stability of the system is enhanced. According to the method, the nonlinear influence of the flow characteristic of the cooling liquid can be comprehensively analyzed, and more efficient heat dissipation optimization is realized.

The application relates to a causal DAG discovery method for an online service system based on fusion of soft priori. The method comprises the following steps: obtaining observation data and text meta-knowledge of the service system, preprocessing to form a standardized sample set, identifying variable types and semantics and outputting; generating a natural language description according to the variable semantics, querying a large language model for an ordered variable pair, analyzing to obtain three types of causal probability vectors, and calibrating to obtain an edge-level priori probability. An appropriate conditional independence test method is selected, high-confidence independent / dependent sentences are divided, and weights are assigned. A candidate directed acyclic graph is selected as an initial structure, parameters are estimated by linear regression, and data fitting scores are calculated, language priori scores, conditional independence penalty terms and counterfactual self-consistency penalty terms are calculated. Fusion is carried out into a hybrid score function, discrete optimization is carried out under the constraint of a directed acyclic graph, and a causal graph structure with the optimal score is output. The method can improve the efficiency and accuracy of a smart operation and maintenance system.

There is provided a non-transitory computer-readable medium storing a calculation program for causing a computer to execute a process. The process includes, an embedding vector generation process for generating an embedding vector that represents each similarity of each problem for multiple problems, in optimization using a continuous relaxation simulated annealing method, which searches for a solution by incorporating continuous relaxation to discrete optimization problems, and a search process for introducing a loss term based on the embedding vector into a loss function in which each element of a matrix obtained by relaxing discrete variables to be optimized into a continuous matrix becomes a discrete optimization problem, and searching for solutions of the multiple problems in parallel.

This application relates to a model construction method, a multivariate time series anomaly detection method, and related components, belonging to the field of system anomaly detection technology. The method includes: initializing learnable embedding vectors for device nodes; determining hyperedges of a first hypergraph structure based on the similarity between learnable embedding vectors; adjusting the device nodes and hyperedges in the first hypergraph structure based on an approximate discrete optimization mechanism to obtain a second hypergraph structure; modeling the learnable embedding vectors, the second hypergraph structure, and the multivariate time series to generate a spatiotemporal hypergraph neural network model; combining the spatiotemporal hypergraph neural network model and a fully connected neural network to generate an initial anomaly detection model; and training and validating the initial anomaly detection model using a target dataset and a mean squared error loss function to generate a target anomaly detection model. This application can improve detection efficiency and accuracy.

The present invention proposes an automatic high-pressure die-casting gate design method based on computer simulation, comprising: obtaining a two-dimensional projection of a three-dimensional model, including: projecting the three-dimensional grid corresponding to the casting onto six planes in a Cartesian coordinate system with the positive and negative directions of the x, y, and z axes as normal vectors; gate parametric design, including: automatically defining the location for placing the gate on the three-dimensional grid corresponding to the casting; gate design optimization, including: generating a parametric gate using a discrete optimization algorithm based on die-casting simulation, including: first randomly generating a series of gate designs, and then using the above-generated gate designs for die-casting simulation calculations; generating a new design using an optimization algorithm based on the final air entrainment corresponding to different designs calculated by simulation to reduce the air entrainment; and after performing a series of iterations, selecting the design with the smallest air entrainment as the final design.